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- README.md +143 -3
- assets/teaser.png +3 -0
- lingbot-map.pt +3 -0
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README.md
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<h1 align="center">LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction</h1>
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<p align="center">
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<a href="lingbot-map_paper.pdf"><img src="https://img.shields.io/static/v1?label=Paper&message=PDF&color=red&logo=arxiv"></a>
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<a href="https://technology.robbyant.com/lingbot-map"><img src="https://img.shields.io/badge/Project-Website-blue"></a>
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<a href=""><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Model&message=HuggingFace&color=orange"></a>
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<a href="LICENSE.txt"><img src="https://img.shields.io/badge/License-Apache--2.0-green"></a>
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</p>
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<p align="center">
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<img src="assets/teaser.png" width="100%">
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</p>
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<p align="center">
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<video src="https://gw.alipayobjects.com/v/huamei_vaouhm/afts/video/q0sdTr9Mm6IAAAAAmyAAAAgADglFAQJr" width="100%" autoplay loop muted playsinline></video>
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</p>
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---
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# Quick Start
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## Installation
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**1. Create conda environment**
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```bash
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conda create -n lingbot-map python=3.10 -y
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conda activate lingbot-map
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```
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**2. Install PyTorch (CUDA 12.8)**
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```bash
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pip install torch==2.9.1 torchvision==0.24.1 --index-url https://download.pytorch.org/whl/cu128
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```
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> For other CUDA versions, see [PyTorch Get Started](https://pytorch.org/get-started/locally/).
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**3. Install lingbot-map**
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```bash
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pip install -e .
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```
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**4. Install FlashInfer (recommended)**
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FlashInfer provides paged KV cache attention for efficient streaming inference:
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```bash
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# CUDA 12.8 + PyTorch 2.9
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pip install flashinfer-python -i https://flashinfer.ai/whl/cu128/torch2.9/
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```
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> For other CUDA/PyTorch combinations, see [FlashInfer installation](https://docs.flashinfer.ai/installation.html).
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> If FlashInfer is not installed, the model falls back to SDPA (PyTorch native attention) via `--use_sdpa`.
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**5. Visualization dependencies (optional)**
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```bash
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pip install -e ".[vis]"
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```
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# Demo
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## Streaming Inference from Images
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--image_folder /path/to/images/
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```
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## Streaming Inference from Video
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--video_path video.mp4 --fps 10
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```
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## Streaming with Keyframe Interval
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Use `--keyframe_interval` to reduce KV cache memory by only keeping every N-th frame as a keyframe. Non-keyframe frames still produce predictions but are not stored in the cache. This is useful for long sequences
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which excesses 320 frames.
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--image_folder /path/to/images/ --keyframe_interval 6
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```
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## Windowed Inference (for long sequences, >3000 frames)
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--video_path video.mp4 --fps 10 \
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--mode windowed --window_size 64
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```
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## With Sky Masking
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--image_folder /path/to/images/ --mask_sky
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```
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## Without FlashInfer (SDPA fallback)
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```bash
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python demo.py --model_path /path/to/checkpoint.pt \
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--image_folder /path/to/images/ --use_sdpa
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```
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# Model Download
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<!-- TODO: fill in model checkpoints -->
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| Model Name | Huggingface Repository | Description |
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| :--- | :--- | :--- |
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| lingbot-map | | Base model checkpoint |
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# License
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This project is released under the Apache License 2.0. See [LICENSE](LICENSE.txt) file for details.
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# Citation
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```bibtex
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@article{lingbot-map2026,
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title={},
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author={},
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journal={arXiv preprint arXiv:},
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year={2026}
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}
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```
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# Acknowledgments
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This work builds upon several excellent open-source projects:
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- [VGGT](https://github.com/facebookresearch/vggt)
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- [DINOv2](https://github.com/facebookresearch/dinov2)
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- [Flashinfer](https://github.com/flashinfer-ai/flashinfer)
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---
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assets/teaser.png
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Git LFS Details
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lingbot-map.pt
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